Vehicle rental businesses, fleet management businesses, and/or vehicle leasing businesses may regularly register data associated with each vehicle owned by said businesses for effective management and operation of the said businesses. For example, the businesses may periodically register a location, a mileage, and/or a condition of a vehicle. Said data associated with vehicles may be also be beneficial to issuers of insurance policies, logistics providers, maintenance business, and other service providers, in addition to States and other agencies, vehicle history providers, and data aggregators.
Typically, to register said data associated with a vehicle, an employee of said businesses, a driver, or a service provider may go to each vehicle, retrieve the relevant data, and manually record the data on a paper form. Then, the data recorded on the paper form may be inputted to an electronic form on a computer. In other words, currently, the registration of data associated with vehicles is done manually and therefore, is inefficient, labor intensive, and prone to human error, particularly when a large number of vehicles are involved.
Automated technologies that move the process of registering data associated with vehicles away from a human user do exist. For example, vehicles are provided with on-board diagnostic ports (OBD) to which telematics devices can be coupled to extract information associated with the vehicles. However, the OBD port of each vehicle may have a proprietary design and therefore, different telematics device hardware may be required for different types of vehicles making said automated solution inefficient and cost-intensive. In another example, computer vision algorithms, such as optical character recognition (OCR) have been used to obtain a mileage of a vehicle from an image of the vehicle's odometer. However, OCR is limited to reading vehicle dashboard components whose values are represented by clearly defined numbers and/or texts. Therefore, OCR cannot be used to obtain data from vehicle dashboard components that are not defined by numbers or texts, such as analog gauges, digital gauges, warning lights, etc. In yet another example, customized or explicit algorithms may be used for analyzing specific components of a specific vehicle's dashboard. However, when numerous makes of vehicles are involved, where each make has numerous models and each model has different vehicle dashboard layout and configuration, designing and programming explicit algorithms for each vehicle becomes unfeasible, extremely inefficient, and cost-prohibitive.
In light of the above discussed deficiencies of existing technologies, there exists a need for an improved technology for automated extraction of data associated with a vehicle from an image of the vehicle's dashboard.